1. Field of the Invention
The present invention relates to spoken dialog systems and more specifically to a system and method of detecting, summarizing and reporting information from automated dialog systems.
2. Introduction
Spoken language dialog systems are becoming more popular and affordable for many companies. These systems provide users and customers with a voice interface to a company wherein the user can speak in a natural manner to provide and receive information. As these spoken dialog systems become more common, much information about their operation becomes available from their system logs. AT&T's VoiceTone® service is an example of a voice dialog system that can be developed for a particular company or purpose. Since these systems are automated, they can provide a huge amount of data on system operation which has application for a number of different audiences.
The basic components of a spoken dialog service include an automatic speech recognition (ASR) module, a spoken language understanding (SLU) module, a dialog manager (DM), a language generation (LG) module and a text-to-speech (TTS) module. These components work together to provide the spoken dialog interaction with the user. These are shown in FIG. 1 and the features associated with the invention are explained more fully below. A spoken dialog system enables a service provider to hear the speech provided by the user, convert the speech to text and understand the meaning or user's intended message in the speech, and to generate an appropriate response.
As a spoken dialog service is deployed, a large quantity of data regarding the user conversations may be logged to obtain information about the behavior of the system and how people interact with the system. This data can provide important details about trends and developments in the area of customer relations management. One of the downfalls of such a system, however, is that human agents are no longer involved in the interactive process and it becomes more difficult to gauge the user experience with the automated system. The automated system has a more difficult time distinguishing the level of user dissatisfaction or satisfaction with the system without engaging in a dialog to request that information.
Further, as companies deploy more automated systems for handling interactions (telephone calls, e-mails, instant-messaging (IM) sessions, chats, etc.) with their customers, the opportunities for human agents to track customer satisfaction decrease. Much of the current analysis of spoken dialog systems focuses on how to deploy and tune the system, which information does not provide helpful business-related data. Therefore, what is needed in the art is a system for obtaining information about user satisfaction and other business-related data from an automated spoken dialog service.